Coordinated Planning of Electric Vehicle Charging Infrastructure and Renewables in Power Grids

Wang, Bo; Dehghanian, Payman; Zhao, Dongbo

doi:10.1109/oajpe.2023.3245993

Cited by 11 publications

(3 citation statements)

References 34 publications

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“…One important application is in demand-side management, which uses load curtailment and load shifting as main management methods [7]. Leveraging the fundamental operations outlined, prior studies focused on the optimization of EV charging through an MPC-based methodology to achieve their goals, which primarily fall into two distinct categories: objectives related to operational costs ( [3], [6], [8], [9]) and those aimed at diminishing costs associated with grid impacts ( [10], [11]). Other works tried to combine these two categories such as in [12] and [5].…”

Section: B Literature Reviewmentioning

confidence: 99%

“…The DNN-MPC technique is tested on a real PV-powered Electric Vehicle Supply Equipment (EVSE), where it showed almost similar performance to an EMPC. In [8], the team utilized an IEEE 118-bus test system, which incorporates Renewable Energies (REs), including wind and PV, as well as EVs with significant integration. Operational expenses, in this case, are influenced by EV charging costs based on vehicle type, REs curtailment, the amount of generator fuel utilized, and battery degradation.…”

Section: ) Operational Costmentioning

confidence: 99%

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Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure Management

Mejdi,

Kardous,

Grayaa

2024

IEEE Access

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The rapid expansion of Electric Vehicle (EV) charging capacities, driven by the surge in transportation electrification, has a direct impact on the electric power system, particularly the Low Voltage (LV) grid. Consequently, there is a pressing need to redesign energy management systems, placing a greater emphasis on mitigating the effects of EV charging. In this article, we crafted an online grid-level Model Predictive Control (MPC) incorporating Machine Learning (ML) prediction enhanced with Singular Spectrum Analysis (SSA). ML models predicts the future EV charging demands, which are the key factors for the MPC for managing efficiently a large-scale charging infrastructure, aiming to mitigate EV charging impacts, specifically phase unbalance and overloading. The MPC results, especially on phase unbalance metrics, mirrored in a predictive manner those of offline/no forecast optimization from our previous work. A week-long MPC simulation comparison with uncontrolled case showcased a 63% reduction in peak demand duration, a 68% decrease in Phasing Unbalance Index (PUI), and a 45% reduction in Voltage Unbalance Factor (VUF). The minimum voltage unbalance improved from 0.897 pu to 0.913 pu, reducing out-ofstandard drops duration by 21%. Lastly, we observed a 20% cut in lines' losses, equating to a 50 kgCO 2 reduction within a week.

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Section: B Literature Reviewmentioning

confidence: 99%

Section: ) Operational Costmentioning

confidence: 99%

Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure Management

Mejdi,

Kardous,

Grayaa

2024

IEEE Access

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“…Particle swarm optimization [66,88,112,121,128,160,162,164,165,191] Genetic algorithm [36,76,129,183] Virus colony search algorithm [84] Harris hawks algorithm [38] Grey wolf optimization algorithm [96] Cuckoo search algorithm [98] Artificial bee colony algorithm [102,128] Extreme learning machine algorithm [107] Alternating direction method of multipliers [111] Model predictive control learning [116] Parameter adaptive differential evolution algorithm [117] Self-adaptive imperialist competitive algorithm [148] Gravitational search algorithm [151] Table 9. Cont.…”

Section: Mathematical Algorithmsmentioning

confidence: 99%

Integration of Renewable Energy and Electric Vehicles in Power Systems: A Review

et al. 2023

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Electric vehicles (EVs) represent a promising green technology for mitigating environmental impacts. However, their widespread adoption has significant implications for management, monitoring, and control of power systems. The integration of renewable energy sources (RESs), commonly referred to as green energy sources or alternative energy sources, into the network infrastructure is a sustainable and effective approach to addressing these matters. This paper provides a comprehensive review of the integration of RESs and EVs into power systems. The bibliographic analysis revealed that IEEE Access had the highest impact among journals. In order to enhance the classification of the reviewed literature, we have provided an analytical summary of the contributions made by each paper. The categorization facilitated the recognition of the primary objectives explored in the reviewed works, including the classification of EVs and RESs, the incorporation of RESs and EVs into power systems with an emphasis on emissions, the establishment of EV charging stations and parking facilities, EV batteries and battery energy storage systems, strategies for managing the integration of RESs with EVs, EV aggregators, and the financial implications. In order to provide researchers with a valuable synopsis of the implementation particulars, the papers were bifurcated into two primary classifications, namely mathematical algorithms and heuristic algorithms. The mixed integer linear programming algorithm and particle swarm optimization algorithm were commonly utilized formulations in optimization. MATLAB/Simulink was the primary platform used for executing a considerable portion of these algorithms, with CPLEX being the dominant optimization tool. Finally, this study offers avenues for further discourse and investigation regarding areas of research that remain unexplored.

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A dynamic pricing strategy and charging coordination of PEV in a renewable-grid integrated charging station

Choudhary,

Mahanty,

Kumar

2025

Electric Power Systems Research

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Coordinated Planning of Electric Vehicle Charging Infrastructure and Renewables in Power Grids

Cited by 11 publications

References 34 publications

Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure Management

Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure Management

Integration of Renewable Energy and Electric Vehicles in Power Systems: A Review

A dynamic pricing strategy and charging coordination of PEV in a renewable-grid integrated charging station

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